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This paper investigates the best arm identification (BAI) problem instochastic multi-armed bandits in the fixed confidence setting. The generalclass of the exponential family of bandits is considered. The existingalgorithms for the exponential family of bandits face computational challenges.To mitigate these challenges, the BAI problem is viewed and analyzed as asequential composite hypothesis testing task, and a framework is proposed thatadopts the likelihood ratio-based tests known to be effective for sequentialtesting. Based on this test statistic, a BAI algorithm is designed thatleverages the canonical sequential probability ratio tests for arm selectionand is amenable to tractable analysis for the exponential family of bandits.This algorithm has two key features: (1) its sample complexity isasymptotically optimal, and (2) it is guaranteed to be $\delta-$PAC. Existingefficient approaches focus on the Gaussian setting and require Thompsonsampling for the arm deemed the best and the challenger arm. Additionally, thispaper analytically quantifies the computational expense of identifying thechallenger in an existing approach. Finally, numerical experiments are providedto support the analysis.

SPRT-based Efficient Best Arm Identification in Stochastic Bandits